This paper proposes a new AC voltage sensorless control scheme for three-phase pulse-width modulation rectifier. A new startup process to ensure a smooth starting of the system is also proposed. The sensorless control scheme uses an adaptive neural (AN) estimator inserted in voltage-oriented control to eliminate the grid voltage sensors. The developed AN estimator combines an adaptive neural network in series with an adaptive neural filter. The AN estimator structure leads to simple, accurate and fast grid voltages estimation, and makes it ideal for low cost digital signal processor implementation. Lyapunov based stability and parameters tuning of the AN estimator are performed. Simulation and experimental tests are carried out to verify the feasibility and effectiveness of the AN estimator. Obtained results show that; the proposed AN estimator presented faster convergence and better accuracy than the second order generalized integrator based estimator; the new startup procedure avoided the over-current and reduced the settling time; the AN estimator presented high performances even under distorted and unbalanced grid voltages.

In this paper, a new neural network-based virtual flux (NN-VF) estimator is proposed for sensorless control of a pulse-width modulation rectifier under unbalanced and distorted grid conditions. In this estimator, the VF vector is reconstructed through an emulated ideal integrator. Thereafter, positive and negative sequence VF components are extracted using a NNbased positive and negative sequence components separator. Lyapunov’s theory-based convergence analysis is performed for optimal tuning of the NN-VF estimator. Accordingly, accurate and fast estimation is achieved. A VF-based predictive direct power control (VF-PDPC) model including the estimated VF positive sequence components is formulated. A startup procedure is considered for smooth starting under unbalanced grid conditions. Feasibility and robustness of the developed VF-PDPC are verified by experiments. Mainly, obtained results demonstrate that the startup procedure reduces settling time and avoids over currents; the VF-PDPC achieves better current waveforms than those obtained by the conventional PDPC under unbalanced grid conditions; and the NN-VF estimator presents similar dynamic performances as the second-order generalized integrator which uses grid voltages measurement.

This paper presents the design and performance evaluation of an experimental platform that emulates the static and dynamic behavior of a 3 kW Wind Energy Conversion System (WECS). The platform includes a wind turbine emulator (WTE) using a separately excited DC motor (SEDCM) as the prime mover, coupled with a grid-connected doubly-fed induction generator (DFIG). This setup enables comprehensive laboratory studies of a WECS without the need for large-scale field installations. A novel inertia compensation strategy is implemented to ensure the SEDCM accurately replicates the power and torque characteristics of a real wind turbine across various wind profiles. The DFIG was chosen for its high efficiency at variable wind speeds and its reduced power converter requirements compared to other generators. The control strategy for the DFIG is detailed, highlighting its performance and seamless integration within the system. Unlike most studies focusing on generators connected to simple loads, this research considers a grid-connected system, which introduces additional challenges and requirements. This study thoroughly investigates the grid-connected converter, addressing specific demands for grid connection and ensuring compliance with grid standards. Experimental results validate the effectiveness of the emulator, demonstrating its potential as a key tool for optimizing wind turbine control strategies and real-time algorithm validation, and enhancing the performance and reliability of renewable energy systems.

In this article, a frequency-adaptive neural network-based virtual flux (FANN-VF) estimator is developed for sensorless control of a pulsewidth modulation converter under unbalanced and distorted grid conditions. This method exploits two parallel FANNs configured as quadrature signal generators. The VF fundamental positive and negative sequence components` are inherently separated in the estimator without employing any cascaded filters. A frequency-locked loop is introduced to accurately track the frequency variations. The estimated VF PNSCs are directly exploited to compute the power references in VF-based predictive direct power control scheme. The developed strategy ensures constant active power and sinusoidal current waveforms under nonideal grid conditions. Simulation and experimental tests are performed to verify the effectiveness of the proposed method.

In this paper, an adaptive voltage-oriented control (VOC) for three-phase grid-connected pulse-width modulation (PWM) rectifier is developed. As the control loop behavior is affected by the PWM rectifier operating conditions, the effects associated to the PWM rectifier losses and uncertainty in the inductance should be inserted in the model. Thus, equivalent resistance and inductance are estimated online by means of an adaptive linear neuron (ADALINE). The proposed ADALINE estimator is inserted in the current control loop to perform an adaptive VOC scheme. Hence, an online updating of the decoupling terms and the current controller gains is achieved. The ADALINE capability to estimate accurately the equivalent inductance and resistance as well as the adaptive VOC scheme performances are finally evaluated by experiments. Experimental results show improved performances and accurate estimation.

Precise knowledge of amplitude, phase and frequency of the grid voltages is essential for grid synchronization of distributed generation units. Nevertheless, grid parameters estimation becomes complex under nonideal conditions. Therefore, a novel frequency adaptive neural network-based frequency-locked loop (FANN-FLL) strategy for unbalanced and distorted conditions is proposed in this paper. The FANN-FLL is based on a FANN configured as a quadrature signal generator, allowing fast and accurate fundamental symmetrical components estimation. To make the system frequency adaptive, the FANN weight vector is exploited in a new frequency estimation method. Simulation tests under highly unbalanced and distorted grid conditions are carried out. Obtained results demonstrated the superiority of the FANN-FLL compared with the decoupled double synchronous reference frame phase-locked loop.